This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Normative aging in humans and other primates leads to the accumulation of DNA lesions, progressive degeneration of DNA, and the functional impairment and death of neurons. Recent whole genome studies have found that normative human senescence includes down-regulation of genes whose transcripts play critical roles in nuclear DNA repair pathways. Erroneous DNA repair has been linked to the instability of the highly variable expanded repeat sequences causing a variety of genetically transmitted human neurological diseases. This study advances an ongoing project developing and implementing stochastic mathematical models that analyze existing allele frequency data measuring progressive mutational instability of pathogenic microsatellites in somatic tissues. Our current generation of continuous-time Markov chain models has revealed previously unknown characteristics of the mutation process by examining a single large data set for a single disease (Friedreich ataxia). This success has lead us to obtain data sets for other diseases (Huntington's and SCA 7) suitable for similar modeling analysis. The modeling process requires a complex and expensive nonlinear optimization involving repeated evaluation of model fit as parameters vary over a large, mixed continuous-discrete, feasible parameter space of from 4 to 10 dimensions. Three distinct approaches will be explored to facilitate the analysis at reduced expense: development of novel hybrid optimization algorithms that employ "natural" (e.g. "genetic'and +swarm algorithms in conjunction with traditional optimization methods, use of Monte-Carlo simulations, and high throughput (parallel) computation. The algorithms developed will be tested against several existing data sets, both suggesting new insights into the age dependence of the DNA repair process and providing new tools with which to study the repair mechanisms underlying DNA microsatellite somatic instability.